EpiCompare Report
6-08-2022
EpiCompare compares epigenetic datasets for quality control and benchmarking purposes. The report consists of three sections:
- General Metrics: Metrics on peaks (percentage of blacklisted and non-standard peaks, and peak widths) and fragments (duplication rate) of samples.
- Peak Overlap: Percetnage and statistical significance of overlapping and non-overlapping peaks. Also includes upset plot.
- Functional Annotation: Functional annotation (ChromHMM, ChIPseeker and enrichment analysis) of peaks. Also includes peak enrichment around TSS.
Input Datasets
- Reference peakfile: H3K9ac_EN4G38_E2_IR12
- Total of 11 peak files:
## [1] "File1: H3K9ac_EN4G38_E1_IRpseudo1"
## [1] "File2: H3K9ac_EN4G38_E1_IRpseudo2"
## [1] "File3: H3K9ac_EN4G38_E1_IRpseudo12"
## [1] "File4: H3K9ac_EN4G38_E1_IR12"
## [1] "File5: H3K9ac_EN3G38_E1_IR12"
## [1] "File6: H3K9ac_EN3G38_E1_I1"
## [1] "File7: H3K9ac_EN3G38_E1_I12"
## [1] "File8: H3K9ac_EN4G38_E2_IRpseudo1"
## [1] "File9: H3K9ac_EN4G38_E2_IRpseudo2"
## [1] "File10: H3K9ac_EN4G38_E2_IRpseudo12"
## [1] "File11: H3K9ac_EN4G38_E2_IR12"Code
EpiCompare(peakfiles = list(H3K9ac_EN4G38_E1_IRpseudo1, H3K9ac_EN4G38_E1_IRpseudo2, H3K9ac_EN4G38_E1_IRpseudo12, H3K9ac_EN4G38_E1_IR12, H3K9ac_EN3G38_E1_IR12, H3K9ac_EN3G38_E1_I1, H3K9ac_EN3G38_E1_I12, H3K9ac_EN4G38_E2_IRpseudo1, H3K9ac_EN4G38_E2_IRpseudo2, H3K9ac_EN4G38_E2_IRpseudo12, H3K9ac_EN4G38_E2_IR12),
genome_build = hg38,
blacklist = blacklist,
picard_files = list(),
reference = H3K9ac_EN4G38_E2_IR12,
stat_plot = TRUE,
chromHMM_plot = TRUE,
chromHMM_annotation = "K562",
chipseeker_plot = TRUE,
enrichment_plot = TRUE,
interact = TRUE,
save_output = TRUE,
output_dir = "/Users/apple/Downloads/cutandrun/ENCODE_correlation/K562_H3K9ac")1. General Metrics
Peak Information
Column Description:
- PeakN before tidy: Total number of peaks including those blacklisted and those in non-standard chromosomes.
- Blacklisted peaks removed (%): Percentage of blacklisted peaks in samples. E.g. ENCODE hg19 blacklist includes regions in the hg19 genome that have anomalous and/or unstructured signals independent of the cell-line or experiment.
- Non-standard peaks removed (%): Percentage of peaks in non-standard
and/or mitochondrial chromosomes. Identified using
BRGenomics::tidyChromosomes().
- PeakN after tidy: Total number of peaks after removing those in blacklisted regions and non-standard chromosomes.
NB: EpiCompare uses filtered peakfiles (i.e. datasets after removing peaks in blacklisted regions and non-standard chromosomes)
| Sample | PeakN before tidy | Blacklisted peaks removed (%) | Non-standard peaks removed (%) | PeakN after tidy |
|---|---|---|---|---|
| H3K9ac_EN4G38_E1_IRpseudo1 | 77823 | 0.00000 | 0.00000 | 77823 |
| H3K9ac_EN4G38_E1_IRpseudo2 | 105157 | 0.00000 | 0.00000 | 105157 |
| H3K9ac_EN4G38_E1_IRpseudo12 | 120499 | 0.00000 | 0.00000 | 120499 |
| H3K9ac_EN4G38_E1_IR12 | 94198 | 0.00000 | 0.00000 | 94198 |
| H3K9ac_EN3G38_E1_IR12 | 133713 | 0.01120 | 0.00972 | 133685 |
| H3K9ac_EN3G38_E1_I1 | 178786 | 0.00783 | 0.01400 | 178747 |
| H3K9ac_EN3G38_E1_I12 | 187696 | 0.00799 | 0.00852 | 187665 |
| H3K9ac_EN4G38_E2_IRpseudo1 | 33747 | 0.00000 | 0.00000 | 33747 |
| H3K9ac_EN4G38_E2_IRpseudo2 | 38899 | 0.00000 | 0.00000 | 38899 |
| H3K9ac_EN4G38_E2_IRpseudo12 | 38252 | 0.00000 | 0.00000 | 38252 |
Fragment Information
Metrics on fragments is shown only if Picard summary is provided. See manual for help.
Column Description:
- Mapped_Fragments: Number of mapped read pairs in the file.
- Duplication_Rate: Percentage of mapped sequence that is marked as duplicate.
- Unique_Fragments: Number of mapped sequence that is not marked as duplicate.
Peak widths
Distribution of peak widths in samples
2. Peak Overlap
Percentage Overlap
Percentage of overlapping peaks between samples. Hover over heatmap for percentage values.
N.B. How to interpret heatmap: [Samples in x-axis of heatmap] peaks in [Samples in y-axis of heatmap] peaks
Upset Plot
Upset plot of overlapping peaks between samples. See here on how to interpret the plot.
quartz_off_screen
2
Statistical Significance
Depending on the format of the reference file, EpiCompare outputs different plots:
- Reference dataset has BED6+4 format (peakcalling performed with MACS2): EpiCompare generates paired boxplot per sample showing the distribution of -log10(q-value) of reference peaks that are overlapping and non-overlapping with the sample dataset.
- Reference dataset does not have BED6+4 format: EpiCompare generates a barplot of percentage of overlapping sample peaks with the reference, coloured by statistical significance (adjusted p-value) of the overlap.
Reference peakfile: H3K9ac_EN4G38_E2_IR12
Keys:
- Overlap: Sample peaks in Reference peaks
- Unique: Sample peaks not in Reference peaks
3. Functional Annotation
3.1 ChromHMM
ChromHMM annotates and characterises peaks into different chromatin states. ChromHMM annotations used in EpiCompare were obtained from here.
- Cell-type annotation file used in this analysis: K562
All samples
ChromHMM annotation of individual samples.
Overlap: Sample peaks in Reference peaks
Percentage of Sample peaks found in Reference peaks (Reference peakfile: H3K9ac_EN4G38_E2_IR12)
| Percentage | |
|---|---|
| H3K9ac_EN4G38_E1_IRpseudo1 | 33.8 |
| H3K9ac_EN4G38_E1_IRpseudo2 | 34.9 |
| H3K9ac_EN4G38_E1_IRpseudo12 | 29.8 |
| H3K9ac_EN4G38_E1_IR12 | 28.0 |
| H3K9ac_EN3G38_E1_IR12 | 25.7 |
| H3K9ac_EN3G38_E1_I1 | 19.8 |
| H3K9ac_EN3G38_E1_I12 | 20.9 |
| H3K9ac_EN4G38_E2_IRpseudo1 | 86.4 |
| H3K9ac_EN4G38_E2_IRpseudo2 | 88.4 |
| H3K9ac_EN4G38_E2_IRpseudo12 | 83.9 |
| H3K9ac_EN4G38_E2_IR12 | 100.0 |
ChromHMM annotation of sample peaks found in reference peaks.
Overlap: Reference peaks in Sample peaks
Percentage of Reference peaks found in Sample peaks (Reference peakfile: H3K9ac_EN4G38_E2_IR12)
| Percentage | |
|---|---|
| H3K9ac_EN4G38_E1_IRpseudo1 | 53.5 |
| H3K9ac_EN4G38_E1_IRpseudo2 | 74.7 |
| H3K9ac_EN4G38_E1_IRpseudo12 | 72.9 |
| H3K9ac_EN4G38_E1_IR12 | 60.6 |
| H3K9ac_EN3G38_E1_IR12 | 70.9 |
| H3K9ac_EN3G38_E1_I1 | 60.7 |
| H3K9ac_EN3G38_E1_I12 | 75.5 |
| H3K9ac_EN4G38_E2_IRpseudo1 | 77.1 |
| H3K9ac_EN4G38_E2_IRpseudo2 | 76.1 |
| H3K9ac_EN4G38_E2_IRpseudo12 | 90.6 |
| H3K9ac_EN4G38_E2_IR12 | 100.0 |
ChromHMM annotation of reference peaks found in sample peaks.
Unique: Sample peaks not in Reference peaks
Percentage of sample peaks not found in reference peaks (Reference peakfile: H3K9ac_EN4G38_E2_IR12)
| Percentage | |
|---|---|
| H3K9ac_EN4G38_E1_IRpseudo1 | 66.2 |
| H3K9ac_EN4G38_E1_IRpseudo2 | 65.1 |
| H3K9ac_EN4G38_E1_IRpseudo12 | 70.2 |
| H3K9ac_EN4G38_E1_IR12 | 72.0 |
| H3K9ac_EN3G38_E1_IR12 | 74.3 |
| H3K9ac_EN3G38_E1_I1 | 80.2 |
| H3K9ac_EN3G38_E1_I12 | 79.1 |
| H3K9ac_EN4G38_E2_IRpseudo1 | 13.6 |
| H3K9ac_EN4G38_E2_IRpseudo2 | 11.6 |
| H3K9ac_EN4G38_E2_IRpseudo12 | 16.1 |
| H3K9ac_EN4G38_E2_IR12 | 0.0 |
ChromHMM annotation of sample peaks not found in reference peaks.
Unique: Reference peaks not in Sample peaks
Percentage of reference peaks not found in sample peaks (Reference peakfile: H3K9ac_EN4G38_E2_IR12)
| Percentage | |
|---|---|
| H3K9ac_EN4G38_E1_IRpseudo1 | 46.50 |
| H3K9ac_EN4G38_E1_IRpseudo2 | 25.30 |
| H3K9ac_EN4G38_E1_IRpseudo12 | 27.10 |
| H3K9ac_EN4G38_E1_IR12 | 39.40 |
| H3K9ac_EN3G38_E1_IR12 | 29.10 |
| H3K9ac_EN3G38_E1_I1 | 39.30 |
| H3K9ac_EN3G38_E1_I12 | 24.50 |
| H3K9ac_EN4G38_E2_IRpseudo1 | 22.90 |
| H3K9ac_EN4G38_E2_IRpseudo2 | 23.90 |
| H3K9ac_EN4G38_E2_IRpseudo12 | 9.38 |
| H3K9ac_EN4G38_E2_IR12 | 0.00 |
ChromHMM annotation of reference peaks not found in sample peaks.
3.2 ChIPseeker
EpiCompare uses ChIPseeker::annotatePeak() to annotate
peaks with the nearest gene and genomic regions where the peak is
located. The peaks are annotated with genes taken from human genome
annotations (hg19 or hg38) provided by Bioconductor.
3.3 Functional Enrichment Analysis
EpiCompare performs KEGG pathway and GO enrichment analysis using
clusterProfiler. ChIPseeker::annotatePeak() is
first used to assign peaks to nearest genes. Biological themes amongst
the genes are identified using ontologies (KEGG and GO). The peaks are
annotated with genes taken from annotations of human genome (hg19 or
hg38) provided by Bioconductor.
KEGG
GO
3.4 Peak Frequency around TSS
This plots peaks that are mapping to transcriptional start sites (TSS). TSS regions are defined as the flanking sequence of the TSS sites. The frequency of peaks in downstream (-3000bp) and upstream (+3000bp) of TSS is plotted. Faint color line around the main frequency line represents the 95% confidence interval estimated by bootstrap method.
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4. Session Info
## R version 4.2.0 (2022-04-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Monterey 12.3.1
##
## Matrix products: default
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] zh_CN.UTF-8/zh_CN.UTF-8/zh_CN.UTF-8/C/zh_CN.UTF-8/zh_CN.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] rtracklayer_1.56.0 GenomicRanges_1.48.0 GenomeInfoDb_1.32.2 IRanges_2.30.0 S4Vectors_0.34.0 BiocGenerics_0.42.0
## [7] EpiCompare_1.0.0
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.2 RSQLite_2.2.14
## [4] AnnotationDbi_1.58.0 htmlwidgets_1.5.4 grid_4.2.0
## [7] BiocParallel_1.30.3 scatterpie_0.1.7 munsell_0.5.0
## [10] codetools_0.2-18 colorspace_2.0-3 GOSemSim_2.22.0
## [13] TxDb.Hsapiens.UCSC.hg38.knownGene_3.15.0 Biobase_2.56.0 filelock_1.0.2
## [16] highr_0.9 knitr_1.39 rstudioapi_0.13
## [19] DOSE_3.22.0 labeling_0.4.2 MatrixGenerics_1.8.0
## [22] GenomeInfoDbData_1.2.8 polyclip_1.10-0 seqPattern_1.28.0
## [25] bit64_4.0.5 farver_2.1.0 downloader_0.4
## [28] vctrs_0.4.1 treeio_1.20.0 generics_0.1.2
## [31] xfun_0.31 BiocFileCache_2.4.0 R6_2.5.1
## [34] graphlayouts_0.8.0 locfit_1.5-9.5 bitops_1.0-7
## [37] BRGenomics_1.8.0 cachem_1.0.6 fgsea_1.22.0
## [40] gridGraphics_0.5-1 DelayedArray_0.22.0 assertthat_0.2.1
## [43] vroom_1.5.7 promises_1.2.0.1 BiocIO_1.6.0
## [46] scales_1.2.0 ggraph_2.0.5 enrichplot_1.16.1
## [49] gtable_0.3.0 tidygraph_1.2.1 rlang_1.0.2
## [52] genefilter_1.78.0 splines_4.2.0 lazyeval_0.2.2
## [55] impute_1.70.0 plyranges_1.16.0 BiocManager_1.30.18
## [58] yaml_2.3.5 reshape2_1.4.4 crosstalk_1.2.0
## [61] GenomicFeatures_1.48.3 httpuv_1.6.5 qvalue_2.28.0
## [64] clusterProfiler_4.4.2 tools_4.2.0 ggplotify_0.1.0
## [67] gridBase_0.4-7 ggplot2_3.3.6 ellipsis_0.3.2
## [70] gplots_3.1.3 jquerylib_0.1.4 RColorBrewer_1.1-3
## [73] Rcpp_1.0.8.3 plyr_1.8.7 progress_1.2.2
## [76] zlibbioc_1.42.0 purrr_0.3.4 RCurl_1.98-1.6
## [79] prettyunits_1.1.1 viridis_0.6.2 SummarizedExperiment_1.26.1
## [82] ggrepel_0.9.1 magrittr_2.0.3 data.table_1.14.2
## [85] DO.db_2.9 matrixStats_0.62.0 evaluate_0.15
## [88] hms_1.1.1 patchwork_1.1.1 mime_0.12
## [91] xtable_1.8-4 XML_3.99-0.9 gridExtra_2.3
## [94] compiler_4.2.0 biomaRt_2.52.0 tibble_3.1.7
## [97] KernSmooth_2.23-20 crayon_1.5.1 shadowtext_0.1.2
## [100] htmltools_0.5.2 ggfun_0.0.6 later_1.3.0
## [103] tzdb_0.3.0 tidyr_1.2.0 geneplotter_1.74.0
## [106] aplot_0.1.6 DBI_1.1.2 tweenr_1.0.2
## [109] ChIPseeker_1.32.0 genomation_1.28.0 dbplyr_2.2.0
## [112] MASS_7.3-57 rappdirs_0.3.3 boot_1.3-28
## [115] Matrix_1.4-1 readr_2.1.2 cli_3.3.0
## [118] parallel_4.2.0 igraph_1.3.1 pkgconfig_2.0.3
## [121] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 GenomicAlignments_1.32.0 plotly_4.10.0
## [124] xml2_1.3.3 ggtree_3.4.0 annotate_1.74.0
## [127] bslib_0.3.1 XVector_0.36.0 yulab.utils_0.0.4
## [130] stringr_1.4.0 digest_0.6.29 Biostrings_2.64.0
## [133] rmarkdown_2.14 fastmatch_1.1-3 tidytree_0.3.9
## [136] restfulr_0.0.14 curl_4.3.2 shiny_1.7.1
## [139] Rsamtools_2.12.0 gtools_3.9.2.1 rjson_0.2.21
## [142] lifecycle_1.0.1 nlme_3.1-157 jsonlite_1.8.0
## [145] viridisLite_0.4.0 BSgenome_1.64.0 fansi_1.0.3
## [148] pillar_1.7.0 lattice_0.20-45 KEGGREST_1.36.0
## [151] fastmap_1.1.0 httr_1.4.3 plotrix_3.8-2
## [154] survival_3.3-1 GO.db_3.15.0 interactiveDisplayBase_1.34.0
## [157] glue_1.6.2 UpSetR_1.4.0 png_0.1-7
## [160] BiocVersion_3.15.2 bit_4.0.4 sass_0.4.1
## [163] ggforce_0.3.3 stringi_1.7.6 blob_1.2.3
## [166] DESeq2_1.36.0 org.Hs.eg.db_3.15.0 AnnotationHub_3.4.0
## [169] caTools_1.18.2 memoise_2.0.1 dplyr_1.0.9
## [172] ape_5.6-2